Summary of Video Diffusion Alignment Via Reward Gradients, by Mihir Prabhudesai and Russell Mendonca and Zheyang Qin and Katerina Fragkiadaki and Deepak Pathak
Video Diffusion Alignment via Reward Gradients
by Mihir Prabhudesai, Russell Mendonca, Zheyang Qin, Katerina Fragkiadaki, Deepak Pathak
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to adapting video diffusion models for specific downstream tasks is proposed, leveraging pre-trained reward models that learn preferences on top of powerful vision discriminative models. By backpropagating gradients from the reward models to the video diffusion model, efficient learning in complex search spaces like videos becomes possible. The method demonstrates improved efficiency in terms of reward queries and computation compared to prior gradient-free approaches. Experiments across various reward models and video diffusion models show promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has made a breakthrough in developing video diffusion models that can be easily adapted for specific tasks. Normally, adapting these models requires collecting large datasets of videos, which is time-consuming and difficult. To solve this problem, they used pre-trained models that learn what makes certain videos good or bad, and then applied these lessons to the video diffusion model. This new approach allows the video diffusion model to learn more efficiently and effectively, making it a major step forward in the field. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
